With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics and information retrieval. Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete. Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques. First we summarise the stateof-the-art databases and the performance metrics for face age estimation. Then we review the age estimation techniques based on three categories of face features (local, global and hybrid) and discuss different types of age learning algorithms. Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation.

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